Current evolutionary algorithms or other genetic resources may incorporate epsilon non-dominance to determine a set of solutions that satisfies one or more criteria important to the decision maker. One such criterion is a desired quantity of solutions obtained in the converged result. Another might be a minimum spacing between solutions in the objective vector space for the converged result. In general, a static epsilon vector will typically not allow for the satisfaction of any a priori criterion without intensive hand-tuning and trial and error. The hand-tuning process of the epsilon vector is inefficient, time consuming, and subject to user error. Accordingly, there is an opportunity for systems and methods for auto-adaptive control over converged results for multi-dimensional optimization.